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Proceedings Papers

Learning and Engaging Through Transformative Informal Setting Driven by COVID Induced Leading Edge Virtual Technology for Advanced Fluid Mechanics Modeling and Simulations Seminar Series

Demy Rodriguez, Leslie Trevizo, Arturo Rodriguez, Julio Aguilar, Clinton Chijioke, Brandon Paez, Rafael Baez, V. M. Krushnarao Kotteda, Vinod Kumar
Proc. ASME. FEDSM2022, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC), V001T03A016, August 3–5, 2022
Paper No: FEDSM2022-87409
DOI: https://doi.org/10.1115/FEDSM2022-87409
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Topics: Engineering simulation, Fluid mechanics, Modeling, Pandemics, Simulation
Proceedings Papers

FSI of a Cantilever Beam: FVM-FEM and Neural Network Analysis

Clinton Chijioke, Arturo Rodriguez, Andres Enriquez, Vinod Kumar, Vivek Tandon, Jose Terrazas, Daniel Villanueva, V. M. Krushnarao Kotteda
Proc. ASME. FEDSM2022, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC), V001T03A025, August 3–5, 2022
Paper No: FEDSM2022-87636
DOI: https://doi.org/10.1115/FEDSM2022-87636
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Topics: Artificial neural networks, Cantilever beams, Computational fluid dynamics, Finite element methods, Finite volume methods, Fluid structure interaction
Proceedings Papers

Atmospheric Turbulence Intermittency Effects on Remote Sensing Laser Propagation

Arturo Rodriguez, V. S. Rao Gudimetla, Richard Adansi, Jose Terrazas, Vicente Corral, Christopher Harris, Vinod Kumar, Rafael Baez, Brandon Paez
Proc. ASME. FEDSM2022, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC), V001T03A008, August 3–5, 2022
Paper No: FEDSM2022-86958
DOI: https://doi.org/10.1115/FEDSM2022-86958
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Topics: Lasers, Turbulence, Fluid dynamics, Refractive index, Approximation, Computer simulation, Statistical distributions, Tradeoffs
Proceedings Papers

Physics-Informed Long-Short Term Memory Neural Network Performance on Holloman High-Speed Test Track Sled Study

José Pérez, Rafael Baez, Jose Terrazas, Arturo Rodríguez, Daniel Villanueva, Olac Fuentes, Vinod Kumar, Brandon Paez, Abdiel Cruz
Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A014, August 3–5, 2022
Paper No: FEDSM2022-86953
DOI: https://doi.org/10.1115/FEDSM2022-86953
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Topics: Artificial neural networks, Physics, Simulation, Braking, Fluid dynamics, Architecture, Computational fluid dynamics, Errors, Fluids, Geometry
Proceedings Papers

Aerodynamic Performance of Symmetric and Cambered Airfoils at Low Reynolds Numbers

Abdiel Cruz, Arturo Rodriguez, V. M. Krushnarao Kotteda, Brandon Paez, Daniel Villanueva, Clinton Chijioke, Jose Terrazas, Vinod Kumar
Proc. ASME. FEDSM2022, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC), V001T03A009, August 3–5, 2022
Paper No: FEDSM2022-86960
DOI: https://doi.org/10.1115/FEDSM2022-86960
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Topics: Aerodynamics, Airfoils, Reynolds number, Drag (Fluid dynamics), Simulation, Approximation, Computational fluid dynamics, Computer software, Mach number, Vehicles
Proceedings Papers

Field Predictions of Hypersonic Cones Using Physics-Informed Neural Networks

Daniel Villanueva, Brandon Paez, Arturo Rodriguez, Ashesh Chattopadhyay, V. M. Krushnarao Kotteda, Rafael Baez, Jose Perez, Jose Terrazas, Vinod Kumar
Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A015, August 3–5, 2022
Paper No: FEDSM2022-86957
DOI: https://doi.org/10.1115/FEDSM2022-86957
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Topics: Artificial neural networks, Physics, Compressible flow, Computational fluid dynamics, Differential equations, Dimensions, Flow (Dynamics), Fluid dynamics, Inverse problems, Mach number
Proceedings Papers

Mesh Adaptability Technique for Canonical Turbulent Jet Flows via Reinforcement Learning

Brandon Paez, Arturo Rodriguez, V. M. Krushnarao Kotteda, Ashesh Chattopadhyay, Jose Terrazas, Rafael Baez, Vinod Kumar
Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A021, August 3–5, 2022
Paper No: FEDSM2022-87413
DOI: https://doi.org/10.1115/FEDSM2022-87413
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Topics: Flow (Dynamics), Reinforcement learning, Turbulence
Proceedings Papers

Is it Turbulent or Laminar? Convolutional Neural Network Predictions

Nicholas Dudu, Arturo Rodriguez, V. M. Krushnarao Kotteda, Jose Terrazas, Daniel Villanueva, Clinton Chijioke, Rafael Baez, Brandon Paez, Vinod Kumar
Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A020, August 3–5, 2022
Paper No: FEDSM2022-87412
DOI: https://doi.org/10.1115/FEDSM2022-87412
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Topics: Artificial neural networks, Turbulence, Flow (Dynamics), Machine learning, Boundary layers, Cylinders, Laminar flow, Simulation, Trains
Proceedings Papers

Three-Dimensional Two-Phase Flow Simulations of Water Braking Phenomena for High-Speed Test Track Sled

Jose Terrazas, Arturo Rodriguez, Vinod Kumar, Richard Adansi, V. M. Krushnarao Kotteda
Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A009, August 10–12, 2021
Paper No: FEDSM2021-65799
DOI: https://doi.org/10.1115/FEDSM2021-65799
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Topics: Braking, Computational fluid dynamics, Engineering simulation, Multiphase flow, Simulation, Two-phase flow, Water, Brakes, Inflow, Vehicles
Proceedings Papers

Fractal and Convolutional Analysis for Deep Atmospheric Turbulence Using Machine Learning

Nicholas Dudu, Arturo Rodriguez, Gael Moran, Jose Terrazas, Richard Adansi, V. M. Krushnarao Kotteda, Christopher Harris, Vinod Kumar
Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A051, August 10–12, 2021
Paper No: FEDSM2021-65798
DOI: https://doi.org/10.1115/FEDSM2021-65798
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Topics: Fractals, Machine learning, Turbulence, Dimensions, Artificial neural networks, Flow (Dynamics), Large eddy simulation, Scalars, Shapes, Trains
Proceedings Papers

Aerodynamic Performance of Design for a CO 2 Dragster

Brandon Paez, Arturo Rodriguez, Nicholas Dudu, Jose Terrazas, Richard Adansi, V. M. Krushnarao Kotteda, Julio C. Aguilar, Vinod Kumar
Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A040, August 10–12, 2021
Paper No: FEDSM2021-65793
DOI: https://doi.org/10.1115/FEDSM2021-65793
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Topics: Carbon dioxide, Design, Fluid structure interaction
Proceedings Papers

Performing Fourier Transform on a Velocity Profile From Atmospheric Turbulence Studies

Richard Adansi, Jose Terrazas, Arturo Rodriguez, V. M. Krushnarao Kotteda, Vinod Kumar, Aldo Rubio, Edgar Avalos
Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A035, August 10–12, 2021
Paper No: FEDSM2021-65812
DOI: https://doi.org/10.1115/FEDSM2021-65812
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Topics: Fourier transforms, Turbulence
Proceedings Papers

Causal Inference Analysis to Find Relationships Found in Boundary-Layer Transition – Part I: Theoretical

Arturo Rodriguez, Jose Terrazas, Richard Adansi, V. M. Krushnarao Kotteda, Jorge A. Munoz, Vinod Kumar
Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A050, August 10–12, 2021
Paper No: FEDSM2021-61843
DOI: https://doi.org/10.1115/FEDSM2021-61843
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Topics: Boundary layers, Turbulence, Flow (Dynamics), Computational fluid dynamics, Fluctuations (Physics), Reynolds number, Vehicles, Defense industry, Design, Fluid dynamics
Proceedings Papers

A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed

Julio Aguilar, Laura Sandoval, Arturo Rodriguez, Sanjay Shantha Kumar, Jose Terrazas, Richard Adansi, Vinod Kumar, Arturo Bronson
Proc. ASME. FEDSM2021, Volume 3: Fluid Mechanics; Micro and Nano Fluid Dynamics; Multiphase Flow, V003T08A020, August 10–12, 2021
Paper No: FEDSM2021-65794
DOI: https://doi.org/10.1115/FEDSM2021-65794
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Topics: Alloys, Artificial neural networks, Equilibrium (Physics), Fluid dynamics, High temperature, Metals, Porosity, Scanning electron microscopes, Temperature, Vehicles
Proceedings Papers

Stochastic Analysis of LES Atmospheric Turbulence Solutions With Generative Machine Learning Models

Arturo Rodriguez, Carlos R. Cuellar, Luis F. Rodriguez, Armando Garcia, V. S. Rao Gudimetla, V. M. Krushnarao Kotteda, Jorge A. Munoz, Vinod Kumar
Proc. ASME. FEDSM2020, Volume 1: Fluid Applications and Systems; Fluid Measurement and Instrumentation, V001T01A001, July 13–15, 2020
Paper No: FEDSM2020-20127
DOI: https://doi.org/10.1115/FEDSM2020-20127
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Topics: Machine learning, Turbulence
Proceedings Papers

Parameter Sensitivity and Statistical Correlation Found in Atmospheric Turbulence Studies

Luis F. Rodriguez, Vinod Kumar, Arturo Rodriguez, V. M. Krushnarao Kotteda, V. S. Rao Gudimetla, Jorge A. Munoz
Proc. ASME. FEDSM2020, Volume 2: Fluid Mechanics; Multiphase Flows, V002T03A041, July 13–15, 2020
Paper No: FEDSM2020-20254
DOI: https://doi.org/10.1115/FEDSM2020-20254
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Topics: Computational fluid dynamics, Turbulence
Proceedings Papers

Trilinos Solvers Scalability on a MFiX-Trilinos Framework Applied to Fluidized Bed Simulations

Arturo Rodriguez, V. M. Krushnarao Kotteda, Luis F. Rodriguez, Vinod Kumar, Jorge A. Munoz
Proc. ASME. FEDSM2020, Volume 2: Fluid Mechanics; Multiphase Flows, V002T04A033, July 13–15, 2020
Paper No: FEDSM2020-20250
DOI: https://doi.org/10.1115/FEDSM2020-20250
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Topics: Engineering simulation, Fluidized beds, Multiphase flow, Simulation, Computational fluid dynamics, FORTRAN, Linear systems, Algorithms, Architecture, Flow (Dynamics)
Proceedings Papers

Branch and Bound Analysis to Characterize Phase Variations in Laser Propagation Through Deep Turbulence

Luis F. Rodriguez, Vinod Kumar, Jose Espiritu, Arturo Bronson, Krushnarao Kotteda, Diego Lozano, Arturo Rodriguez
Proc. ASME. AJKFluids2019, Volume 2: Computational Fluid Dynamics, V002T02A080, July 28–August 1, 2019
Paper No: AJKFluids2019-5567
DOI: https://doi.org/10.1115/AJKFluids2019-5567
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Topics: Lasers, Turbulence, Machine learning, Diffraction, Laser beams, Phase (Wave motion), Waves
Proceedings Papers

Machine Learning Approach to Predict the Flow Rate for an Immiscible Two-Phase Flow at Pore Scale for Enhanced Oil Recovery Application

Arturo Rodriguez, V. M. Krushnarao Kotteda, Luis F. Rodriguez, Vinod Kumar, Arturo Schiaffino, Zackery R. Nieto
Proc. ASME. FEDSM2018, Volume 2: Development and Applications in Computational Fluid Dynamics; Industrial and Environmental Applications of Fluid Mechanics; Fluid Measurement and Instrumentation; Cavitation and Phase Change, V002T09A003, July 15–20, 2018
Paper No: FEDSM2018-83050
DOI: https://doi.org/10.1115/FEDSM2018-83050
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Topics: Flow (Dynamics), Machine learning, Tertiary petroleum recovery, Two-phase flow, Petroleum extraction, Pressure drop, Surface tension, Viscosity, Algorithms, Brain
Proceedings Papers

Utilization of Machine Learning to Predict the Surface Tension of Metals and Alloys

Zackery Nieto, V. M. Krushnarao Kotteda, Arturo Rodriguez, Sanjay Shantha Kumar, Vinod Kumar, Arturo Bronson
Proc. ASME. FEDSM2018, Volume 3: Fluid Machinery; Erosion, Slurry, Sedimentation; Experimental, Multiscale, and Numerical Methods for Multiphase Flows; Gas-Liquid, Gas-Solid, and Liquid-Solid Flows; Performance of Multiphase Flow Systems; Micro/Nano-Fluidics, V003T21A003, July 15–20, 2018
Paper No: FEDSM2018-83248
DOI: https://doi.org/10.1115/FEDSM2018-83248
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Topics: Alloys, Machine learning, Metals, Surface tension, Algorithms, Copper, Silver, Computers, Liquid alloys, Neural network models